Keeping AI-generated code auditable
How a task-graph approach records every AI action against the work it served, so AI-assisted delivery stays reviewable and trustworthy.
As AI writes more code, teams need to answer a simple question: how did this get built? Without a record, AI-assisted work becomes a black box. A task graph solves this by linking every requirement, spec, task, artifact, and agent run.
What the graph records
Each requirement leads to a spec, which breaks into tasks. Each task links to the artifacts produced for it and the agent runs that produced them. The result is a lineage from business intent to the exact change that fulfilled it.
Why it matters
Auditability supports code review, compliance, and onboarding. When someone asks why a change exists, the graph answers with the requirement and the agent run behind it — not guesswork.
FAQ
- Does this slow developers down?
- No — the record is captured as work happens (for example, from Git commits that reference a task), so traceability is a by-product of normal work rather than extra effort.
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